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Found 5 papers, 2 papers with code
Metalearners for Ranking Treatment Effects
We propose an alternative approach based on learning to rank.
Learning continuous-valued treatment effects through representation balancing
In this work, we propose CBRNet, a causal machine learning approach to estimate an individual dose response from observational data.
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time
Machine learning (ML) holds great potential for accurately forecasting treatment outcomes over time, which could ultimately enable the adoption of more individualized treatment strategies in many practical applications.
Prescriptive maintenance with causal machine learning
This work proposes to relax both assumptions by learning the effect of maintenance conditional on a machine's characteristics from observational data on similar machines using existing methodologies for causal inference.
A new perspective on classification: optimally allocating limited resources to uncertain tasks
A central problem in business concerns the optimal allocation of limited resources to a set of available tasks, where the payoff of these tasks is inherently uncertain.
